Cingulate dynamics track depression recovery with deep brain stimulation

Sankaraleengam Alagapan, Ki Sueng Choi, Stephen Heisig, Patricio Riva-Posse, Andrea Crowell, Vineet Tiruvadi, Mosadoluwa Obatusin, Ashan Veerakumar, Allison C. Waters, Robert E. Gross, Sinead Quinn, Lydia Denison, Matthew O’Shaughnessy, Marissa Connor, Gregory Canal, Jungho Cha, Rachel Hershenberg, Tanya Nauvel, Faical Isbaine, Muhammad Furqan Afzal, Martijn Figee, Brian H. Kopell, Robert Butera, Helen S. Mayberg () and Christopher J. Rozell ()
Additional contact information
Sankaraleengam Alagapan: Georgia Institute of Technology
Ki Sueng Choi: Icahn School of Medicine at Mount Sinai
Stephen Heisig: Icahn School of Medicine at Mount Sinai
Patricio Riva-Posse: Emory University School of Medicine
Andrea Crowell: Emory University School of Medicine
Vineet Tiruvadi: Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University
Mosadoluwa Obatusin: Icahn School of Medicine at Mount Sinai
Ashan Veerakumar: Schulich School of Medicine and Dentistry at Western University
Allison C. Waters: Icahn School of Medicine at Mount Sinai
Robert E. Gross: Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University
Sinead Quinn: Emory University School of Medicine
Lydia Denison: Emory University School of Medicine
Matthew O’Shaughnessy: Georgia Institute of Technology
Marissa Connor: Georgia Institute of Technology
Gregory Canal: Georgia Institute of Technology
Jungho Cha: Icahn School of Medicine at Mount Sinai
Rachel Hershenberg: Emory University School of Medicine
Tanya Nauvel: Icahn School of Medicine at Mount Sinai
Faical Isbaine: Emory University School of Medicine
Muhammad Furqan Afzal: Icahn School of Medicine at Mount Sinai
Martijn Figee: Icahn School of Medicine at Mount Sinai
Brian H. Kopell: Icahn School of Medicine at Mount Sinai
Robert Butera: Georgia Institute of Technology
Helen S. Mayberg: Icahn School of Medicine at Mount Sinai
Christopher J. Rozell: Georgia Institute of Technology

Nature, 2023, vol. 622, issue 7981, 130-138

Abstract: Abstract Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient’s current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.

Date: 2023
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DOI: 10.1038/s41586-023-06541-3

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